@inproceedings{ding2023insightpilot,
author = {Ding, Rui and Han, Shi and Zhang, Dongmei},
title = {InsightPilot: An LLM-Empowered Automated Data Exploration System},
organization = {ACL special interest group on linguistic data (SIGDAT)},
booktitle = {EMNLP 2023},
year = {2023},
month = {December},
abstract = {Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset, the user intent and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming.

To address this issue, we introduce InsightPilot, an LLM (Large Language Model)-based, automated data exploration system designed to simplify the data exploration process. InsightPilot features a set of carefully designed analysis actions that streamline the data exploration process. Given a natural language question, InsightPilot collaborates with the LLM to issue a sequence of analysis actions, explore the data and generate insights. We demonstrate the effectiveness of InsightPilot in a user study and a case study, showing how it can help users gain valuable insights from their datasets.},
url = {http://approjects.co.za/?big=en-us/research/publication/insightpilot-an-llm-empowered-automated-data-exploration-system/},
}